Given a CSV file with columns product,price,quantity, which code snippet correctly creates a dictionary mapping each product to its total value (price multiplied by quantity) using csv.DictReader?
hard📝 Application Q8 of 15
Python - Structured Data Files
Given a CSV file with columns product,price,quantity, which code snippet correctly creates a dictionary mapping each product to its total value (price multiplied by quantity) using csv.DictReader?
Aimport csv
with open('products.csv') as f:
reader = csv.reader(f)
totals = {row[0]: row[1] * row[2] for row in reader}
Bimport csv
with open('products.csv') as f:
reader = csv.DictReader(f)
totals = {row['product']: float(row['price']) * int(row['quantity']) for row in reader}
Cimport csv
with open('products.csv') as f:
reader = csv.DictReader(f)
totals = {row['product']: row['price'] + row['quantity'] for row in reader}
Dimport csv
with open('products.csv') as f:
reader = csv.DictReader(f)
totals = {row['product']: int(row['price']) * int(row['quantity']) for row in reader}
Step-by-Step Solution
Solution:
Step 1: Use csv.DictReader to read rows as dicts
This allows accessing columns by name.
Step 2: Convert price and quantity to numeric types
Price may be float, quantity integer; convert accordingly before multiplication.
Step 3: Create dictionary comprehension mapping product to total value
Multiply converted price and quantity for each row.
Final Answer:
import csv
with open('products.csv') as f:
reader = csv.DictReader(f)
totals = {row['product']: float(row['price']) * int(row['quantity']) for row in reader} correctly implements these steps.
Quick Check:
Convert strings to numbers before multiplying [OK]
Quick Trick:Convert strings to numbers before math operations [OK]
Common Mistakes:
Using csv.reader instead of DictReader
Not converting strings to numeric types before multiplication
Adding price and quantity instead of multiplying
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